The field of medical image segmentation is witnessing significant advancements, driven by innovations in deep learning and transformer-based models. Recent developments emphasize the integration of domain-specific knowledge, such as anatomical constraints and semantic guidance, to enhance segmentation accuracy and robustness. Techniques like hybrid residual transformers and semantic-guided models are pushing the boundaries of volumetric medical image segmentation, addressing challenges related to computational efficiency and feature representation. Additionally, the adaptation of general-purpose segmentation models like SAM to specialized medical datasets through fine-tuning and prompt engineering is gaining traction, offering scalable solutions for diverse medical imaging tasks. Notably, the incorporation of language-guided segmentation and multi-level contrastive alignments is bridging the gap between image and text modalities, enabling more precise and context-aware segmentation. These innovations not only improve the accuracy of lesion and tissue segmentation but also facilitate the development of unified models capable of handling heterogeneous data and multiple segmentation tasks. Furthermore, the exploration of memory mechanisms and learnable prompting strategies in semi-supervised learning scenarios is enhancing the generalization and adaptability of models with limited labeled data. Overall, the field is moving towards more intelligent, context-aware, and efficient segmentation solutions that promise to revolutionize clinical practice and medical research.
Advances in Intelligent and Efficient Medical Image Segmentation
Sources
Novel 3D Binary Indexed Tree for Volume Computation of 3D Reconstructed Models from Volumetric Data
QSM-RimDS: A highly sensitive paramagnetic rim lesion detection and segmentation tool for multiple sclerosis lesions
Hyper-Fusion Network for Semi-Automatic Segmentation of Skin Lesions
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation
Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment
Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing
HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation
A Mapper Algorithm with implicit intervals and its optimization
RADARSAT Constellation Mission Compact Polarisation SAR Data for Burned Area Mapping with Deep Learning
SAMIC: Segment Anything with In-Context Spatial Prompt Engineering
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation
SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation
PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model
S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer
Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis
Pitfalls of topology-aware image segmentation